Scalable multi-radio communication in modular robots

Kuo, Victor

doi:10.1016/j.robot.2013.08.007

Cited by 7 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, decentralised coordination algorithms assume over-simplified communication models, which lead to large communication overheads. This hinders the suitability for many of these algorithms for realistic scenarios where communication resources are limited [2], [3], [4], the network is unreliable [5], [6], [7], [8], or the signals are susceptible to interference from other robots [9]. These issues are compounded even further for larger teams [10].…”

Section: Introductionmentioning

confidence: 99%

Planning-Aware Communication for Decentralised Multi-Robot Coordination

Best

Forrai²,

Mettu

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

We present an algorithm for selecting when to communicate during online planning phases of coordinated multi-robot missions. The key idea is that a robot decides to request communication from another robot by reasoning over the predicted information value of communication messages over a sliding time-horizon, where communication messages are probability distributions over action sequences. We formulate this problem in the context of the recently proposed decentralised Monte Carlo tree search (Dec-MCTS) algorithm for online, decentralised multi-robot coordination. We propose a particle filter for predicting the information value, and a polynomialtime belief-space planning algorithm for finding the optimal communication schedules in an online and decentralised manner. We evaluate the benefit of informative communication planning for a multi-robot information gathering scenario with 8 simulated robots. Our results show reductions in channel utilisation of up to four-fifths with surprisingly little impact on coordination performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Planning-Aware Communication for Decentralised Multi-Robot Coordination

Best

Forrai²,

Mettu

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…Multi-radio multi-channel networks (Wu et al, 2000; Xing et al, 2007) can significantly increase network capacity by using multiple communication channels in parallel. Our previous work has shown that a single channel may be reused in a neighbor-to-neighbor architecture while avoiding mutual interference (Kuo and Fitch, 2014). Whereas the aim of these approaches is to maximize the throughput available from source to destination, this paper takes a complementary view.…”

Section: Related Workmentioning

confidence: 99%

Communication-aware information gathering with dynamic information flow

Kassir

Sukkarieh

2014

The International Journal of Robotics Research

View full text Add to dashboard Cite

We are interested in the problem of how to improve estimation in multi-robot information gathering systems by actively controlling the rate of communication between robots. Communication is essential in such systems for decentralized data fusion and decision-making, but wireless networks impose capacity constraints that are frequently overlooked. In order to make efficient use of available capacity, it is necessary to consider a fundamental trade-off between communication cost, computation cost and information value. We introduce a new problem, dynamic information flow, that formalizes this trade-off in terms of decentralized constrained optimization. We propose algorithms that dynamically adjust the data rate of each communication link to maximize an information gain metric subject to constraints on communication and computation resources. The metric is balanced against the communication resources required to transmit data and the computation cost of processing sensor data to form observations. The optimization process selectively routes raw sensor data or processed observation data to zero, one or many robots. Our algorithms therefore allow large systems with many different types of sensors and computational resources to maximize information gain performance while satisfying realistic communication constraints. We also present experimental results with multiple ground robots and multiple sensor types that demonstrate the benefit of dynamic information flow in comparison to simpler bandwidth-limiting methods.

show abstract